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arXiv 2024年

作者： Wei, Hexiang Jiao, Jianhao Hu, Xiangcheng Yu, Jingwen Xie, Xupeng Wu, Jin Zhu, Yilong Liu, Yuxuan Wang, Lujia Liu, Ming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Robot Perception and Learning Lab Department of Computer Science University College London United Kingdom Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology China Guangzhou China

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises 27 sequences, spanning over 2.5 hours and collected from four distinct platforms: a handheld suite, a legged robot, a unmanned ground vehicle (UGV), and a vehicle. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of 38.7km. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately 0.3km2. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad application beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The completae dataset, including sensor data, GT, and calibration details, is accessible at https://***/dataset/fusionportable v2. Copyright © 2024, The Authors. All rights reserved.

关键词： SLAM robotics

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Enhanced Soil Property Estimations from Earth Observation Data with Differential Evolution-Based Multi-Objective TSK Model

Enhanced Soil Property Estimations from Earth Observation Da...

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Congress on Evolutionary Computation

作者： Nikolaos L. Tsakiridis Nikiforos Samarinas Eleni Kalopesa John B. Theocharis George C. Zalidis Department of Agriculture Spectra Lab Group Laboratory of Remote Sensing Spectroscopy and GIS Aristotle University of Thessaloniki Thessaloniki Greece Automation and Robotics Laboratory Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

This paper introduces a novel approach integrating Differential Evolution (DE) with multi-objective optimization techniques for enhancing Type-1 Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems to attain both fair predictive performance and enhanced interpretability within the context of explainable artificial intelligence. The developed approach encodes the whole knowledge base into one chromosome and endeavors to concurrently optimize the prediction accuracy, the number of rules, and the selection of relevant input features. The methodology was tested on Earth observation data for the estimation of soil health parameters via infrared spectroscopy, and more concretely i) on the 2015 LUCAS topsoil dataset to predict three key soil properties (namely, soil organic carbon, clay content, and pH) from laboratory spectra, and ii) on a set of PRISMA hyperspectral images to predict soil organic carbon in a region of northern Greece. Compared to the classical Random Forest (RF) algorithm, our proposed learning algorithm attains a fair balance between accuracy and interpretability, and is statistically equivalent to RF in terms of accuracy. The findings underscore the potential of the proposed methodology in refining TSK models and its applicability in Earth observation-driven predictions, paving the way for both enhanced modeling accuracy and sparse feature selection.

关键词： Earth Radio frequency Accuracy Soil properties Estimation Predictive models Prediction algorithms

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HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re-Identification Implemented on Tour-Guide Robot

HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Lin, Zheyuan Huang, Min Wang, Wen Qin, Mengjie Li, Te Wan, Minhong Gu, Jason Zhu, Shiqiang Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

For service robots, person re-identification (ReID) and multi-pedestrian tracking (MPT) are vital to get a person's location and link identities across frames. Though their accuracy keeps improving, most work lacks consideration of the application scenario, haunted by limited space, constrained power supply, and demands of the real-time response during human-robot interaction. Some ReID models learn trivial or unrelated features, inhibiting the downstream tasks. To solve these issues, the efficient and light-weighted Head-Shoulder Mask aided ResNet (HSMR) is proposed. This model applies multi-task learning to enhance the feature extraction performance in the training stage without extra computational load during inference. The auxiliary task fully uses head-shoulder information to guide the network and focuses on the head region, which contains the identity information. In experiments on the Tour-Guide Robot Data Base (TGRDB), HSMR earned results better than ResNet-18 on the ReID task and was superior to the recent two-stream method on the MPT task. On the mobile hardware, inference reaches an average of 15.2 FPS, three times faster than the two-stream method. The code is released at https://***/ZhYLin99/HSMR. © 2022 IEEE.

关键词： Human robot interaction

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Low-cost Mars Terrain Classification System Based on Coarse-grained Annotation

Low-cost Mars Terrain Classification System Based on Coarse-...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhang, Jian Chen, Yeheng Zhu, Shiqiang Li, Yuehua Xie, Tian Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-grained training samples, while the undefined terrains on Mars are difficult to be classified or fine-grained labeled. Actually, most of the terrain categories can only be coarse-grained labeled due to several limitations, such as overlapped sub-regions, blurred borders, etc. To solve this problem, CACMT (Coarse-grained Annotation-based Classification for Mars Terrain) is proposed to generate the global fine-grained classification map from the local coarse-grained data. Specifically, the complete pipeline is decomposed into (i) annotation rules with unique design, (ii) hierarchical feature fusion network for predicting sub-features of terrain (iii) and a generator for outputting dense terrain categories of Mars. Finally, the results of actual data on Mars demonstrate that the terrain sub-features can be successfully recognized and a dense terrain classification map can be generated applying only coarse-grained labeled images. © 2022 IEEE.

关键词： Motion planning

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Enhanced Detection of Demagnetization in Direct-Drive Wind Turbine Generators Using eMIST and Machine Learning

Enhanced Detection of Demagnetization in Direct-Drive Wind T...

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IEEE Workshop on electrical Machines Design, Control and Diagnosis (WEMDCD)

作者： Qiuyi Chen Panagiotis Tsilifis Jaime Renedo Anglada Department of Mechanical Engineering University of Maryland College Park MD USA Robotics AI & Computer Vision Group GE Vernova Advanced Research Niskayuna NY USA Electrical Systems GE Vernova Advanced Research Niskayuna NY USA

ISBN: (数字)9798331520748

ISBN: (纸本)9798331520755

The rapid growth of offshore wind energy needs robust condition monitoring to ensure the reliability and efficiency of direct-drive permanent magnet synchronous generators (PMSGs). Demagnetization, a critical fault in these generators, can severely impact performance and increase operational costs, especially in harsh marine environments. Traditional diagnostic techniques often struggle to detect subtle magnetic property changes under varying load conditions. This paper presents an innovative framework for demagnetization detection in PMSGs, leveraging the electrical Multi-phase Imbalance Separation Technique (eMIST) combined with machine learning models. Using a dataset from offshore wind turbines, including simulated faulty conditions, the proposed methodology enhances anomaly detection by isolating imbalance signatures and correlating them with key operational parameters. Gaussian Process Regression (GPR) is employed to predict anomalies, offering high accuracy and confidence in fault classification. Results demonstrate the approach's ability to identify early-stage demagnetization, enabling predictive maintenance, reducing downtime, and improving the economic viability of offshore wind farms.

关键词： Costs Accuracy Wind energy Machine learning Gaussian processes Wind farms Generators Wind turbines Demagnetization Anomaly detection

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Sparkling hot spots in perovskite solar cells under reverse bias

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ChemPhysMater 2022年第1期1卷 71-76页

作者： Weiqi Li Keqing Huang Jianhui Chang Caiqi Hu Caoyu Long Hai Zhang Xavier Maldague Biao Liu Jianqiao Meng Yuxia Duan Junliang Yang School of Physics and Electronics Central South UniversityChangsha 410083China Computer Vision and Systems Laboratory Department of Electrical and Computer EngineeringUniversitéLaval1065av.de la MédecineQuébec(QC)Canada G1V 0A6

Perovskite solar cells(PSCs)are attracting much attention and are on the way to ***,some modules are subject to reverse bias in actual fields,so it is meaningful to investigate the reverse-bias behav-ior of ***,an in-situ temperature and current measurement technique was ***,some hot spots were observed and then quickly disappeared in the reverse biased PSCs,along with the simultaneous increase in the current and local ***,the potential mechanism has been revealed and *** abnormal bulge in the perovskite film was found at the hot ***,the appearance and disappearance of hot spots were perfectly explained by band bending and tunneling current caused by ion ***-tionally,statistical analysis suggested that sparkling hot spots were related to reverse voltage and efficiencies of *** research provides a great significance for the study of PSCs under reverse bias.

关键词： Perovskite solar cells Reverse bias Hot spots Stability

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Design, Dynamic Modeling and Control of a 2-DOF Robotic Wrist Actuated by Twisted and Coiled Actuators

arXiv

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arXiv 2025年

作者： Zhang, Yunsong Zhou, Xinyu Zhang, Feitian Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States

Robotic wrists play a pivotal role in the functionality of industrial manipulators and humanoid robots, facilitating manipulation and grasping tasks. In recent years, there has been a growing interest in integrating artificial muscle-driven actuators for robotic wrists, driven by advancements in technology offering high energy density, lightweight construction, and compact designs. However, in the study of robotic wrists driven by artificial muscles, dynamic model-based controllers are often overlooked, despite their critical importance for motion analysis and dynamic control of robots. This paper presents a novel design of a two-degree-of-freedom (2-DOF) robotic wrist driven by twisted and coiled actuators (TCA) utilizing a parallel mechanism with a 3RRRR configuration. The proposed robotic wrist is expected to feature lightweight structures and superior motion performance while mitigating friction issues. The Lagrangian dynamic model of the wrist is established, along with a nonlinear model predictive controller (NMPC) designed for trajectory tracking tasks. A prototype of the robotic wrist is developed, and extensive experiments are conducted to validate its superior motion performance and the proposed dynamic model. Subsequently, extensive comparative experiments between NMPC and PID controller were conducted under various operating conditions. The experimental results demonstrate the effectiveness and robustness of the dynamic model-based controller in the motion control of TCA-driven robotic wrists. Copyright © 2025, The Authors. All rights reserved.

关键词： Industrial manipulators

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computer vision Approaches for Automated Bee Counting Application

arXiv

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arXiv 2024年

作者： Bilik, Simon Janakova, Ilona Ligocki, Adam Ficek, Dominik Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland

Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset. © 2024, CC BY.

关键词： Object detection

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions 23

Digital Twin Definition for Detecting Abnormal Perspiration ...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

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